Tab. 7:

Advantages and challenges for ProbRA in human health risk assessment

Advantages of ProbRA	Challenges of ProbRA
Improves transparency and credibility by explicit consideration and treatment of all types of uncertainties; clearly structured; integrative and quantitative; allows ranking of issues and results; more information can be obtained by separating variability from uncertainty	Problem of model incompleteness; relatively time-consuming in performing and interpreting – this “might be a fertile ground for endless debate between utility and regulator” (Kafka, 1998); regulatory delays due to the necessity of analyzing numerous scenarios using various models
Cost effective by assuring that resources are focused on essential safety issues, focuses data collection	More complex and time-consuming analysis and decision-making process because more information and insights must be collected, processed and considered for decisions; requires more data than conventional approaches because distributions of values rather than single values are used
More realistic compared to the current deterministic RA: avoids worst-case assumptions, realistic exposure assessments; overall picture of risks in the population and not just of extreme cases; a probabilistic reference dose could help reduce the potentially inaccurate implication of zero risk below the reference dose.	The incompleteness of the model is much more “apparent”
Improves decision support enabling risk managers to evaluate the full range of variability and uncertainty instead of just using point estimates of exposure, effects, and eventually risk.	More complex structure, the assumptions, methods and results are more difficult to understand and require some mathematical education; lack of understanding of the value of ProbRA for decision-making; personnel must be very well-informed scientifically and technologically to produce consistent application of standards; requires a different skill set than used in current evaluations, but limited resources (staff, time, training or methods) are available
Includes a systematic sensitivity analysis of the uncertainties in the input parameters, which identifies the main sources of uncertainty. Sensitivity analysis is the study of how uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input (Saltelli et al., 2008).	Where extremely rare events must be considered, there are problems with the statistical significance of probabilistic data
Application of an optimization process (Apostolakis, 1990)	Validation challenge; what to compare against? Good practices lacking
More effective risk management; enhances safety and helps manage operability; estimating the success of intervention measures is improved	Complicates decision-making where a more comprehensive characterization of the uncertainties leads to a decrease in clarity regarding how to estimate risk for the scenario under consideration
More transparent risk communication: results and decisions can be communicated on a clearly defined basis	Communicating ProbRA and the impact on the decision/policy options is complex; results are characterized as prognostic estimations of what can or cannot happen in future makes understanding difficult and poses a still unresolved issue for many legal environments
Works with limited data: Even if the amount of available adequate probabilistic data is relatively small, the absolute accuracy of the data is not an issue if probabilistic approaches are used as comparative tools, allowing one to make decisions between different design or operation alternatives	Minimum data requirements currently are a topic of debate; any quantitative risk estimate only makes sense when the employed data are statistically significant in a sense (i.e., sufficient observations available) and if they originate from similar events and have been analyzed with respect to a common criterion
Information economy: Enables estimating formally the value of gathering more information; better prioritize information needs by investing in areas that yield the greatest information value	Difficulty to quantify and weigh risks and benefits
	Various communities have unique sets of perspectives, historical practices, terminologies, resources, and propensities, governed by overlapping set(s) of problems and decision-making goals, regulatory requirements, and legislative mandates being addressed, directly or indirectly, by these interrelated communities.